MSSQL Python Server
БесплатноНе проверенMCP server for safely exposing SQL Server database capabilities to LLM clients, with read-only mode, security features, and observability.
Описание
MCP server for safely exposing SQL Server database capabilities to LLM clients, with read-only mode, security features, and observability.
README
MSSQL MCP Python Server
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This is a MCP (Model Context Protocol) server implementation in Python that safely exposes SQL Server database capabilities to LLM clients.
- If you want a complete guide of how to use, click here!
Quick Start
1. Install Dependencies
cd mssql-mcp-python
pip install -r requirements.txt
# or:
uv sync
2. Configure Database
Create .env file:
# For local SQL Server (Linux/Docker)
export MSSQL_CONNECTION_STRING="Driver={ODBC Driver 17 for SQL Server};Server=localhost,1433;Database=master;UID=sa;PWD=YourPassword123"
# Or for Windows Auth
export MSSQL_CONNECTION_STRING="Driver={ODBC Driver 17 for SQL Server};Server=localhost;Database=master;Trusted_Connection=yes"
3. Run the Server
# With stdio transport (for MCP clients)
python -m mssql_mcp.cli
# With custom settings
MSSQL_QUERY_TIMEOUT=60 READ_ONLY=true python -m mssql_mcp.cli --log-level DEBUG
# Or with HTTP transport
python -m mssql_mcp.cli --transport http --bind 0.0.0.0:8080
# Build and run
docker build -t mssql-mcp:latest .
docker run -e MSSQL_CONNECTION_STRING="..." mssql-mcp:latest
# Or with Docker Compose (HTTP transport, reads .env)
cp .env.example .env # then edit connection string
docker compose up -d
4. Test with curl (HTTP mode)
# Health check
curl http://localhost:8080/health
# Readiness check
curl http://localhost:8080/ready
# Server info
curl http://localhost:8080/info
# Prometheus metrics
curl http://localhost:8080/metrics
Available MCP Tools
The server exposes these tools to MCP clients:
1. execute_sql(sql, format="table", timeout=None, max_rows=None)
Execute SELECT queries (or write operations if enabled).
format:"table","json"or"csv".timeout: per-query timeout (seconds), overridesMSSQL_QUERY_TIMEOUTfor slow queries.max_rows: per-query row cap, overridesMAX_ROWS_PER_QUERY.
Input: "SELECT TOP 10 * FROM users", format="json"
Output: JSON rows + summary; truncation is flagged explicitly.
Write statements return the affected-row count.
2. list_schemas()
List all database schemas
Input: (none)
Output: Schema names list
3. list_tables(schema, limit=200)
List tables with optional schema filter
Input: schema="dbo", limit=100
Output: Table list with metadata
4. schema_discovery(schema)
Get full schema metadata (tables, columns, types)
Input: schema="dbo"
Output: JSON with detailed column info
5. describe_table(table)
Describe a single table: columns, types, nullability, primary keys, descriptions
Input: table="dbo.users" (schema prefix optional)
Output: JSON column metadata for that one table
6. get_database_info()
Get server/database metadata
Input: (none)
Output: Database name, version, machine name
7. get_policy_info()
Get current security policy settings
Input: (none)
Output: Policy details (allowed operations, limits)
8. check_db_connection()
Health check for database connectivity
Input: (none)
Output: Connection status
Security Features
✅ Read-Only by Default
- Only SELECT queries allowed unless explicitly enabled
- Writes require
ENABLE_WRITES=true+ADMIN_CONFIRMtoken
✅ SQL Injection Prevention
- Parameterized queries via pyodbc
- Multi-statement query blocking
- Banned keyword detection (DROP, ALTER, EXEC, etc.)
✅ Sensitive Data Protection
- Automatic log redaction (passwords, connection strings)
- Query hashing for safe logging
- No credentials in response bodies
✅ Resource Limits
- Query timeouts (default 30s)
- Row limits (default 50,000 rows)
- Query length limits (50KB)
- Connection pool limits
✅ Audit Trail
- Structured logging with request metadata
- Query metrics and statistics
- Client ID tracking (when provided)
Observability
Prometheus Metrics
Available at GET /metrics (HTTP mode):
mssql_queries_executed_total— Total queries by tool and statusmssql_queries_blocked_total— Blocked queries by reasonmssql_query_duration_seconds— Query latency histogrammssql_query_rows_returned— Result set size histogrammssql_active_queries— Currently executing queriesmssql_server_ready— Server readiness (0/1)
Structured Logs
All logs in JSON format (when LOG_FORMAT=json):
{
"timestamp": "2024-01-15T10:30:00.123456",
"level": "INFO",
"logger": "mssql_mcp.tools",
"message": "Query allowed",
"module": "tools",
"function": "execute_sql",
"line": 42
}
Health Checks
GET /health— Liveness probe (always 200)GET /ready— Readiness probe (200 if DB connected)
Common Tasks
Change Log Level
LOG_LEVEL=DEBUG python -m mssql_mcp.cli
Enable Write Operations
ENABLE_WRITES=true ADMIN_CONFIRM=secret python -m mssql_mcp.cli
The app-level
ENABLE_WRITESswitch is only the first line of defense. The ultimate authority is the permissions of the SQL login you connect as — see credential override below.
Use a Specific SQL Login (credential override)
Each deployment can run under its own SQL login without editing the base
connection string. MSSQL_USER / MSSQL_PASSWORD take precedence over any
UID/PWD embedded in MSSQL_CONNECTION_STRING:
# Base string holds only driver/server/database; identity comes from these:
MSSQL_USER=reporting_ro MSSQL_PASSWORD=secret python -m mssql_mcp.cli
MSSQL_USER,MSSQL_PASSWORD— override the SQL credentials (ideal for secrets).MSSQL_TRUSTED_CONNECTION=true— use Windows/Integrated auth instead (ignores user/password).
Because the connected login's own permissions govern access, connecting with a
read-only login enforces read-only at the database level, regardless of
ENABLE_WRITES. Conversely, allowing writes requires both ENABLE_WRITES=true
and a login that has write permission.
Increase Query Timeout
MSSQL_QUERY_TIMEOUT=120 python -m mssql_mcp.cli
Fix Garbled Non-ASCII Characters (accents, etc.)
Results are decoded using explicit encodings. The defaults work for most SQL Server
setups (NVARCHAR is UTF-16LE, VARCHAR is read as UTF-8). If VARCHAR columns use a
legacy code-page collation, override the narrow encoding:
# e.g. Central-European legacy VARCHAR data
MSSQL_ENCODING=cp1250 python -m mssql_mcp.cli
MSSQL_ENCODING(defaultutf-8) — decoding of narrowSQL_CHAR/VARCHARcolumnsMSSQL_WIDE_ENCODING(defaultutf-16-le) — wideSQL_WCHAR/NVARCHARdecoding and the query/parameter send encoding (SQL Server expects UTF-16LE; sending UTF-8 corrupts accented literals in queries)
Allow External Access (HTTP transport)
By default the server only accepts requests whose Host is localhost or
127.0.0.1 (DNS rebinding protection). To allow access via an external
hostname, set ALLOWED_HOST to that host (without port):
ALLOWED_HOST=mcp.example.com python -m mssql_mcp.cli --transport http --bind 0.0.0.0:8080
This adds the host to both the allowed hosts and the CORS origins list; local access keeps working.
Run Multiple Instances
python -m mssql_mcp.cli --transport http --bind 127.0.0.1:8080
python -m mssql_mcp.cli --transport http --bind 127.0.0.1:8081 # Different port
Установка MSSQL Python Server
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/lorenzouriel/mssql-mcp-pythonFAQ
MSSQL Python Server MCP бесплатный?
Да, MSSQL Python Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для MSSQL Python Server?
Нет, MSSQL Python Server работает без API-ключей и переменных окружения.
MSSQL Python Server — hosted или self-hosted?
Доступен hosted-вариант: Unyly запускает сервер в облаке, локальная установка не обязательна.
Как установить MSSQL Python Server в Claude Desktop, Claude Code или Cursor?
Открой MSSQL Python Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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